From the 1 of 11 linked papers with an AI index.
1 citations · 1 across the 6 of their papers we have counts for
11 papers
Evaluating Epistemic Uncertainty: Beyond OOD Detection and Active Learning
Jakub Paplhám, Willem Waegeman, Eyke Hüllermeier +1
The paper proposes a decision‑theoretic framework for evaluating epistemic uncertainty by measuring its ability to identify reducible error (regret) in selective prediction, and sh…
Optimal Conformal Prediction under Epistemic Uncertainty
Alireza Javanmardi, Soroush H. Zargarbashi, Santo M. A. R. Thies +3
Conformal prediction (CP) is a widely used frequentist framework to quantify uncertainty by constructing prediction sets with user-specified marginal coverage guarantees. In practi…
Position: agentic AI orchestration should be Bayes-consistent
Theodore Papamarkou, Pierre Alquier, Matthias Bauer +27
LLMs excel at predictive tasks and complex reasoning tasks, but many high-value deployments rely on decisions under uncertainty, for example, which tool to call, which expert to co…
Conformal Prediction in Hierarchical Classification with Constrained Representation Complexity
Thomas Mortier, Alireza Javanmardi, Yusuf Sale +2
Conformal prediction has emerged as a widely used framework for constructing valid prediction sets in classification and regression tasks. In this work, we extend the split conform…
When should we trust the annotation? Selective prediction for molecular structure retrieval from mass spectra
Mira Jürgens, Mira Jürgens, Gaetan De Waele +2
Machine learning methods for identifying molecular structures from tandem mass spectra (MS/MS) have advanced rapidly, yet current approaches still exhibit significant error rates.…
Reducing Aleatoric and Epistemic Uncertainty through Multi-modal Data Acquisition
Arthur Hoarau, Benjamin Quost, Sébastien Destercke +1
To generate accurate and reliable predictions, modern AI systems need to combine data from multiple modalities, such as text, images, audio, spreadsheets, and time series. Multi-mo…